708There is a wealth of literature highlighting the ne.docx

708
T
here is a wealth of literature highlighting the
negative physical (eg, type II diabetes, car-
diovascular problems) and psychosocial (eg,
depression, low self-worth) consequences of ado-
lescent obesity.1-3 However, less attention has been
given to the role adolescent weight status plays in
future health-risk behaviors, such as problematic
substance use. With adolescent overweight and
obesity rates remaining high (33.6% overweight,
18.4% obese 12-19 years),4 and substance use
more prevalent in young adulthood than any other
developmental period,5 identification of adolescent
weight status as a predictor of future problematic
substance use behavior is likely to have a signifi-
cant impact on research and clinical work aimed to
reduce multiple health risks in the transition from
adolescence to adulthood.
Adolescence is a crucial period for prevention ef-
forts aimed to reduce problematic substance use in
young adulthood. According to the National Survey
of Drug Use and Health,5 young adults have the
highest rates of current tobacco use (39.5% overall
including 33.5% cigarette use) and illicit drug use
(21.4%), with 19.0% using marijuana in the past
month. Binge drinking has been reported for 39.8%
and heavy alcohol use for 12.1% of 18- to 25-year-

olds. In the past 30 years, many epidemiological
longitudinal studies have identified several key
risk factors for problematic substance use, includ-
ing regular cigarette smoking, binge drinking, and
marijuana use, in adolescence and young adult-
hood. Temperament,6 behavioral disinhibition,7 ex-
ternalizing behaviors,8 poor parental monitoring,9
lack of parental support,10 negative peer interac-
tions,11 and affiliation with deviant peers12 have
been well-established as critical factors involved in
the development of problematic substance use.13-15
Considering the array of risk factors in adolescence
contributing to future problematic substance use,
it is likely that other health-risk conditions, such
as overweight or obesity status, are linked to prob-
lematic substance use behavior.
Little is currently known about the relationship
between adolescent weight status and future prob-
lematic substance use; however, use of an adoles-
cent developmental framework is likely to increase
our understanding of why this relationship may be
a significant one to address. One explanation may
be that a shared underlying factor like impulsivity
may explain co-occurring obesity and problematic
substance use. As children learn to self-regulate
behaviors, those who have difficulties with self-
control are more likely to over-consume energy-
dense food contributing to obesity risk16,17 and en-
gage in antisocial behaviors leading to substance
abuse and dependence.18,19 Although a shared
underlying factor explanation is plausible, under-
standing adolescent behavior without considering
the social context is incomplete.
H. Isabella Lanza, Research Associate and Christine E. Grel-

la, Professor-in-Residence, Semel Institute for Neuroscience
and Human Behavior, University of California, Los Angeles.
Paul J. Chung, Associate Professor, Department of Pediatrics,
University of California, Los Angeles.
Correspondence Dr Lanza: [email protected]
Does Adolescent Weight Status Predict
Problematic Substance Use Patterns?
H. Isabella Lanza, PhD; Christine E. Grella, PhD; Paul J.
Chung, MD
Objectives: To identify underlying pat-
terns of cigarette smoking, alcohol use,
and marijuana use in young adulthood,
and ascertain whether adolescent over-
weight or obesity status predicts prob-
lematic substance use patterns. Methods:
The study included 15,119 participants
from the National Longitudinal Study of
Adolescent Health (Add Health) at Wave
1 (11-19 years) and Wave 3 (18-26 years).
Latent class analysis was conducted. Re-
sults: Participants were classified into a
Low Substance Use (35%), Regular Smok-
ers (12%), High-risk Alcohol use (33%),
or High Substance Use (20%) class. Over-
weight/obese adolescents had a greater
likelihood of belonging to the Regular
Smokers class. Conclusions: Overweight/
obese adolescents are at higher risk of en-
gaging in regular cigarette smoking with-
out problematic alcohol or marijuana use.
Key words: adolescence, alcohol, ciga-
rette smoking, marijuana, obesity, young

adulthood
Am J Health Behav. 2014;38(5):708-716
DOI: http://dx.doi.org/10.5993/AJHB.38.5.8
Lanza et al
Am J Health Behav.™ 2014;38(5):708-716 709 DOI:
http://dx.doi.org/10.5993/AJHB.38.5.8
Critical to the discussion on risk-taking behav-
iors is the knowledge that social standing among
peers is a prominent goal for most adolescents.
Taking into consideration the important of self-
regulation for risk-taking outcomes, Steinberg’s
social neuroscience perspective on adolescent
risk-taking20 posits that increases in risk-taking
are a result of heightened sensitivity to the social
context and its rewards (ie, peer acceptance), as
well as slower-developing self-regulatory processes
linked to rational decision-making. Both failure to
be accepted by peers and desire for higher social
status may increase vulnerability to risk behav-
iors, such as substance use. Earlier work by Tajfel
and Turner21,22 highlights why overweight and
obese adolescents may be more likely to experience
a negative peer context, which increases vulner-
ability to later risk-taking. As adolescents derive
their self-concept from the social group(s) to which
they belong, social status is often achieved by be-
having in ways that are normative for the group.
Adolescents who do not fit the group norm, such
as those who are different in physical appearance
(eg, obese adolescents), are less likely to be ac-

cepted by peers.23,24 Overweight and obese adoles-
cents are indeed at higher risk for peer alienation
and victimization than normal-weight peers.25-27
Those deviating from the group norm may try to
overcome their poor social status by engaging in
behaviors (eg, substance use) that will increase
status among certain social groups, like deviant
peers.28 They also may engage in risky behaviors,
like substance use, as a way to cope with the nega-
tive feelings stemming from poor social status.29,30
It appears that overweight and obese adolescents
may be experiencing a social context and lack of
self-regulation that increases their risk of engag-
ing in problematic substance use as they transi-
tion into adulthood. Prior cross-sectional stud-
ies have provided tentative evidence that higher
weight status in adolescence is related to problem-
atic cigarette smoking and alcohol use. A positive
relationship between cigarette smoking and body
mass index (BMI) has been reported among ear-
ly adolescent Danish boys.31 In a study of Portu-
guese adolescents, obese girls and boys were more
likely to report daily alcohol consumption and fre-
quent drunkenness compared to non-obese ado-
lescents.32 A study of Taiwanese adolescents also
found that girls and boys with higher BMI were
more likely than counterparts to report regular al-
cohol use and cigarette smoking, but not other il-
licit drugs.33 Furthermore, findings using a large
sample of US adolescents reported that obese girls,
but not boys, were more likely to use alcohol and
smoke cigarettes, but not marijuana.34
Findings from longitudinal studies examining
adolescent weight status and substance use are

more inconclusive and have mainly focused on to-
bacco use. A study of early adolescents in the US
indicated that obese girls were less likely to initiate
tobacco use 2 years later compared to non-obese
girls.35 On the other hand, another US study and a
Swedish study found that smoking initiation was
more likely among obese girls compared to non-
obese girls.36,37 No significant relationships were
found among boys. A recent study using 2 samples
of US adolescents indicated that BMI did not pre-
dict alcohol or other substance use 2 years later.38
Similarly, an epidemiological study of rural US
adolescents did not find a significant association
between obesity trajectory and substance use.39
The inconsistent results from these longitudinal
studies call attention to the need for additional
prospective and more comprehensive research.
The current study sought to clarify past find-
ings on adolescent weight status and risk of prob-
lematic substance use by utilizing a large sample
of US adolescents to identify whether overweight
or obesity status in adolescence is a predictor of
unique patterns of problematic substance use in
young adulthood. To achieve this end, a person-
centered approach, latent class analysis (LCA),
was utilized to identify unique patterns of prob-
lematic substance use by considering responses to
multiple items on cigarette smoking, alcohol use,
and marijuana use simultaneously. Prediction of
adolescent weight status to problematic substance
use patterns was then assessed.
METHODS
Participants

The current study used data from The Nation-
al Longitudinal Study of Adolescent Health (Add
Health), consisting of a nationally representative
sample of adolescents in grades 7-12 in the US
during the 1994-95 school year. Participants were
enrolled in 80 high schools and 52 middle schools.
During the initial wave of the study, 20,745 ado-
lescents in grades 7-12 (11-19 years of age) par-
ticipated in both a school survey and in-home in-
terview between April and December 1995. Written
informed consent was obtained from both parent
and adolescent. Participants were eligible to par-
ticipate in the second wave of data collection about
one year later (April-August 1996). A third wave
of in-home interviews occurred from July 2001 to
April 2002, which included 15,197 young adults
aged 18-26 years. Information was collected on
mental and physical health, health-risk behav-
iors, and contextual factors related to family, peer,
school, and neighborhood.
The analytic sample for the current study includ-
ed 15,119 of the 15,197 participants interviewed at
Wave 3 (young adulthood) during 2001-02; we ex-
cluded 78 persons for whom no substance use data
were available. Of these 15,119 young adults, 53%
were women; 54% White, 21% Black, 15% Latino;
7% Asian; 3% other. The average age of participants
at Wave 1 was 16.10 years ± 1.72 and at Wave 3 was
22.47 years ± 1.76. Wave 1 (adolescent) BMI% was
available for 97% of the analytic sample, with 25%
meeting overweight (14%) or obesity (11%) status.
In terms of socio-economic variables, 93% of partic-

Does Adolescent Weight Status Predict Problematic Substance
Use Patterns?
710
ipants in the sample reported mother education at
Wave 1. Of available data, 20% of mothers received
less than a high school education; 34% were high
school graduates; 19% completed some college;
and 28% completed college or beyond. Also, 75% of
parents of participants in the analytic sample com-
pleted an in-home interview at Wave 1, including
annual household income. Of available data, 29%
reported less than $25,000, 33% between $25,000
and $49,999; 23% between $50,000 and $74,999;
and 8% between $75,000 and $99,999.
Measures
Individual characteristics. Adolescents were
asked to report their sex (1 = female, 0 = male)
and race/ethnicity at Wave 1. Ethnicity variables
for African-American, Asian, Latino, and white
were dummy coded (eg, 1 = African-American, 0
= non-African-American). Adolescents also re-
ported on residential mother’s education status at
Wave 1 (1 = less than a high school education; 2
= high school grad; 3 = completed some college; 4
= completed college or beyond), which was used
as a proxy for SES, given that household income
was only available for 75% of participants who had
completed parent interviews at Wave 1.
Weight status. At Wave 1 (11-19 years old), ado-
lescents self-reported their height and weight. Self-

reported height and weight data have been found to
be reliable for 96% of adolescents in the Add Health
sample.40 Height and weight were used to calculate
age- and sex-specific BMI [weight(lbs)/[height(in)2]
x 703] percentiles using the Centers for Disease
Prevention (CDC) 2000 growth charts.41 Obesity
was defined as having a BMI percentile at or above
the 95th percentile, and overweight defined as hav-
ing a BMI percentile at or above the 85th percentile
and below the 95th percentile. Adolescents either
meeting overweight or obesity status were com-
bined to create an overweight/obesity indicator (1 =
overweight or obese, 0 = non-overweight or obese).
Substance use. At Wave 3 (18-26 years), young
adults responses to multiple questions on cigarette
smoking, alcohol use, and marijuana use were in-
cluded in analyses. Five items related to cigarette
smoking were selected, which included whether par-
ticipants had ever: (1) tried cigarette smoking, even
just 1 or 2 puffs (0 = no, 1 = yes); (2) smoked an en-
tire cigarette (0 = no, 1 = yes); (3) smoked cigarettes
regularly, that is, at least 1 cigarette every day for 30
days (0 = no, 1 = yes); and (4) smoked at all in the
past 30 days (0 = no, 1 = yes). They were also asked
(5) how many cigarettes smoked per day in past 30
days, which was recoded into a categorical item (0 =
none, 1 = 1, 2 = 2-9, 3 = 10-20, 4 = 20+ cigarettes).
Six items on alcohol use were included in analy-
ses. Participants were asked: (1) whether they had
drank more than 2 or 3 times since June 1995
(Wave 1) (0 = no, 1 = yes); (2) days consumed al-
cohol in the past year (0 = none, 1 = couple times
a year, 2 = couple times a month, 3 = 1-2 times a
week, 4 = 3-7 times a week); (3) days consumed

5 or more drinks in past 12 months (0 = none, 1
= couple times a year, 2 = once a month or less,
3 = couple times a month/week, 4 = 3-7 times a
week); (4) days consumed 5 or more drinks in the
last 2 weeks (0 = none, 1 = once, 2 = 2-9, 3 = 10+
times); (5) whether they had been drunk in the
past year (0 = none, 1 = couple times a year, 2 =
once a month or less, 3 = couple times a month/
week, 4 = 3-7 times a week); and (6) whether they
had driven while drinking since June 1995 (Wave
1) (0 = no, 1 = yes).
Three items related to marijuana use were also
selected for analyses. Participants were asked: (1)
whether they had used marijuana since June 1995
(Wave 1) (0 = no, 1 = yes); (2) whether they had used
marijuana in the past year (0 = no, 1 = yes); and (3)
number of times marijuana consumed in the past
30 days, which was recoded into a categorical item
(0 = none, 1 = once, 2 = 2-9, 3 = 10+ times).
Planned Analyses
Latent class analysis (LCA) is used to identify
underlying patterns among observed categorical
indicators (eg, substance use behaviors) and clas-
sify individuals who respond similarly into latent
classes.42-44 LCA is an iterative process using full
information maximum likelihood estimation. Us-
ing Mplus version 7,45 model-building steps were
taken to select the best-fitting class model of sub-
stance use in young adulthood and then ascertain
whether adolescent weight status and other in-
dividual characteristics predicted membership in
particular substance use classes.

Statistical indices, parameter estimates, and
practical implications are used to determine the
best-fitting model.43,44,46 The unconditional model is
first specified (ie, 1-class model), which is then used
as a comparison for an increasing number of class-
es until the models specified no longer converge or
have useful application. Statistical indices, like the
Bayesian Information Criterion (BIC)47 and the Lo-
Mendell-Rubin likelihood ratio test (LMR LRT),48 as
well as interpretability of classes are key in deter-
mining model selection. Item-response probabilities
refer to the likelihood that an individual in a given
latent class will endorse a particular item response.
They are used to confirm that individuals in each
latent class have similar response patterns to the
observed indicators and that class response pat-
terns are distinct from each other. After selecting
the best-fitting model, covariates are added to the
model. Logistic regression coefficients are estimated
by setting the beta parameter to 0 for the reference
class; thus, providing an estimation of log-odds that
indicate an endorsement of a covariate for a certain
class relative to the reference class.
Ethnicity, sex, and a proxy for socioeconomic
status (maternal education) were included in the
covariates analysis in addition to weight status,
as each is strongly tied to disparities in obesity
prevalence and substance use risk. Among adoles-
cents, African Americans and Latinos have higher
Lanza et al

overweight and obesity prevalence than Whites,
and boys are more likely to be overweight or obese
compared to girls.4 In young adulthood, men are
more likely to use marijuana, cigarette smoking is
more prevalent among Whites than African Ameri-
cans, and Whites and Latinos report more binge
drinking than African Americans.5 Generally, SES
indicators like parental education and household
income have shown that lower SES is associated
with higher weight status,49 although associations
with substance use are mixed.5
RESULTS
Descriptive Data
Table 1 compares the average rate of overweight/
obese adolescents’ substance use in young adult-
hood to non-overweight/obese adolescents. Across
all cigarette smoking indicators, overweight/obese
adolescents had higher rates compared to non-
overweight/obese adolescents. In contrast, over-
weight/obese adolescents had lower rates of high-
risk alcohol use compared to non-overweight/
obese adolescence. No significant differences were
found between groups for marijuana use.
Latent Class Analysis
Model selection. LCA was conducted to identify
latent classes of cigarette smoking, alcohol use,
and marijuana use in young adulthood. Table 2
presents the statistical fit indices for 5 classes (the
6-class model did not converge). Model selection is

generally based on a scree-like test, in which bet-
ter fitting models are represented where the indices
begin to level off.50 Although the 5-class model had
the lowest values in fit criteria, indices began to
level off significantly after the 3-class model; con-
sequently, the 3-, 4-, and 5-class models were fur-
ther explored before selecting a best-fitting model.
Examination of parameter estimates identified the
4-class model as best-fitting the data in terms of
classifying the underlying heterogeneity of ciga-
rette, alcohol, and marijuana use in the sample.
Class probabilities, homogeneity of item-response
within classes, and distinct item-response patterns
across classes were assessed to determine which
model was most interpretable.43,46 The 3-class mod-
el was able to identify a low and a high substance
use class, but the third class lacked homogeneity.
Two classes in the 5-class model lacked homogene-
ity and there was a lack of distinctiveness between
2 classes. Figure 1 illustrates the item-response
probabilities of each class of the 4-class model.
Classes appear homogenous and distinct, and
class sizes were found to be substantial as well.
Identified classes. Considering item-response
probabilities for each class (Figure 1), classes were
identified based on endorsement of substance use
behaviors. A little over one-third of the participants
(34.7%) reported very low substance use behavior
(Class 1; Low Substance Use). Of these, close to
80% stated that they had never smoked an en-
tire cigarette, over 95% stated they had not been
drunk or binged on alcohol, and almost 90% had
never tried marijuana.

Participants in the Regular Smokers class (Class
2; 12.4%) showed high endorsement of cigarette
use, with 96% reporting having been a regular
smoker at some point (at least 1 cigarette a day for
Table 1
Cigarette, Alcohol, and Marijuana Use in Young Adulthood:
Overweight/Obese vs. Non-overweight/Obese Adolescents
Overweight/Obese Non-overweight/Obese
Cigarette %
Tried cigarette
Smoked entire cigarette***
Smoked regularly***
Smoked past 30 days***
No. cigarettes per day (more than 1)***
74
61
41
35
32
73
59
38
32
29
Alcohol %
Drank since W1**
Alcohol in past year (multiple times per month or week)***
Binge drink in past year (multiple times per month or week) *
Binge drink in past 2 weeks*
Drunk past year (multiple times per month or week***
Drunk driving***

76
40
20
31
16
21
78
46
22
33
19
24
Marijuana %
Marijuana since W1
Marijuana past year
No. of times used marijuana in past year(more than once)
44
31
18
45
32
17
*** p < .001; ** p < .01; * p < .05
Use Patterns?
712

past 30 days) and all reporting smoking in the past
30 days. Over one-third (38%) smoked 2-9 ciga-
rettes and just over half (52%) smoked 10 or more
cigarettes on days smoked in the past 30 days.
Similar to the Low Substance Use class, problem-
atic alcohol use was very low. Also, although 40%
had tried marijuana since the start of the study,
only one-fourth had used marijuana in the past
year.
The High-risk Alcohol Use class (Class 3; 32.6%)
was characterized by problematic alcohol use but
less risky cigarette and marijuana use. Approxi-
mately 70% stated they had used alcohol multi-
ple times a month or week in the past year, about
80% indicated they had engaged in binge drinking
or been drunk in the past year, and 50% stated
they have binged in the last 2 weeks. Also, 35%
reported drunk driving. In regards to cigarette and
marijuana use, few had ever been regular smokers
(16%), and almost none had smoked in the past 30
days. Although 39% reported using marijuana in
the past year, less than one-fourth reported using
more than once in the last year.
Last, the High Substance Use class (Class 4;
Table 2
Latent Class Model Fit Indices (N = 15,119)
Classes
Log
Likelihood

Free
Parameters BIC
Adjusted
BIC AIC
LMR LRT p-
value for k-1
1 -174995.39 30 350279.49 350184.15 350050.78 N/A
2 -146775.79 61 294138.63 293944.78 293673.58 .000
3 -134435.31 92 269756.00 269463.63 269054.62 .000
4 -128469.95 123 258123.61 257732.72 257185.89 .000
5 -123837.44 154 249156.94 248667.54 247982.89 .000
Note.
AIC = Akaike information criterion; BIC = Bayesian
information criterion; LMR LRT = Lo-Mendell-Rubin
likelihood
ratio test.
Figure 1
Substance Use Classes in Young Adulthood: Item-response
Probabilities
0%
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30%
40%

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3-7 times wk/20+
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Once a mo/2-9
Yes/Once/Couple times yr
No/None
Class 1
Low Substance Use
34.7%

Class 2
Regular Smokers
12.4%
Class 3
High-risk Alcohol Use
32.6%
Class 4
High Substance Use
20.2%
Lanza et al
20.2%) represents participants with the highest
levels of problematic substance use. Almost all
(99%) reported to be regular smokers who smoked
in the last 30 days, with over half smoking 10+
cigarettes on days smoked. Over 85% reported
drinking multiple times per month or week in the
past year, and the majority reported binge drinking
and being drunk in the past year (93%) and binge
drinking in the past 2 weeks (74%). Over half (55%)
stated they had driven drunk. Two-thirds (69%) re-

ported marijuana use in the last year, with 45%
using more than once in that period.
Predictors of class membership. Covariates
were added to the LCA model to determine wheth-
er adolescent weight status predicted particular
substance use classes. A covariate model includ-
ing weight status (overweight or obese), sex, race/
ethnicity (African American, Asian, Latino, White),
and maternal education was estimated simultane-
ously using latent multinomial logistic regression
(Table 3). Using the Low Substance Use class as
a reference class, adolescents meeting overweight
or obesity status had a greater likelihood of being
classified into the Regular Smokers class (β =.37,
OR = 1.45, p < .001) than the Low Substance Use
class. Similarly, overweight or obese adolescents
were more likely to belong to the Regular Smokers
class than the High Substance Use (β =.35, OR =
1.42, p < .001) and the High-risk Alcohol Use (β
=.43, OR = 1.54, p < .001) classes.
As for other individual characteristics (see Table
3 for statistical values), women were less likely to be
classified into any problematic substance use class
compared to the Low Substance Use class. African
Americans, Asians, and Latinos were less likely to
be classified into the High Substance Use or Regu-
lar Smokers classes than the Low Substance Use
class, and African Americans and Asians also were
less likely to belong to the High-risk Alcohol Use
class compared to the Low Substance Use class.
On the other hand, Whites were more likely to be-
long to the High-risk Alcohol Use class compared
to the Low Substance Use class. Adolescents re-

siding with mothers with higher education status
were more likely to be classified into the High Sub-
stance Use and High-risk Alcohol Use classes than
the Low Substance Use Class, but were less likely
to belong to the Regular Smokers class than the
Low Substance Use class. The covariate analysis
also was conducted with annual household in-
come, with results unchanged. Annual household
income was not included in the final model because
a significant proportion of the sample (25%) did not
have household income information, as this infor-
mation was collected with parent-report at Wave 1.
DISCUSSION
Sole consideration of average group differences
of substance use rates give an incomplete picture
of the potential risks overweight or obese adoles-
cents face in young adulthood. The average group
differences shown in Table 1 suggest that cigarette
smoking and alcohol use rates only vary slightly be-
tween overweight/obese and non-overweight/obese
groups. Statistical, but not clinically meaningful
differences, may contribute to a false assumption
that weight status is not an important predictor
of substance use. However, with the utilization of
Table 3
Estimated Odds Ratios (OR) of Class Membership in Relation to
Obesity,
Sex, Race/Ethnicity, and Education Indices ased on a
Multinomial Latent
Class Regression Model
Reference class: Low Substance Use (Class 1)

vs Regular Smokers
(Class 2)
vs High-risk Alcohol
Use (Class 3)
vs High Substance Use
(Class 4)
β(SE) OR(95%CI) β(SE) OR(95%CI) β(SE) OR(95%CI)
Overweight/ obese vs
non-overweight/obese .37(.07)*** 1.45(1.26-1.66) -.06(.05)
.95(.85-1.04) .02(.06) 1.02(.91-1.15)
Female vs Male -.20(.06)** .82(.73-.92) -.67(.05)*** .51(.46-
.56) -.95 (.06)*** .39(.34-.44)
African American vs.
non-AA -1.23(.18)*** .29(.21-.42) -1.03(.15)*** .36(.27-.48) -
1.75 (.17)*** .17(.12-.24)
Asian/Pacific Islander
vs non-API -.96(.20)** .38(.26-.57) -.58(.16)*** .56(.41-.77) -
.97(.18)*** .38(.27-.54)
Latino vs non-Latino -1.19(.18)*** .31(.21-.43) -.11(.16)
.90(.65-1.23) -1.08 (.17)*** .34(.24-.47)
White vs non-White -.16(.17) .86(.61-1.19) .29(.15)*
1.34(1.003-1.79) .24(.16) 1.27(.93-1.74)
Higher vs lower
mother education .-.16(.03)*** .85(.80-.90) .20(.02)***
1.22(1.17-1.27) .09(.02)*** 1.10(1.05-1.14)
*** p < .001; ** p < .01; * p < .05
Use Patterns?

714
a person-centered approach, not only were unique
patterns of substance use behavior in young adult-
hood identified, but a more comprehensive assess-
ment of adolescent weight status as a risk factor for
problematic substance use was achieved.
Ultimately, the key relationship between high-
er weight status and problematic substance use
points to regular cigarette smoking. Overweight
or obese adolescents had a greater likelihood of
belonging to the Regular Smokers class in young
adulthood than any other class. On the other
hand, overweight or obesity status in adolescence
did not predict greater likelihood of belonging to
the High-risk Alcohol Use of High Substance Use
classes; consequently, higher weight status does
not appear to be a risk factor for problematic al-
cohol or marijuana use. Also, given that over-
weight or obese adolescents did not have a lower
likelihood of belonging to the Low Substance Use
class vs problematic substance use classes, higher
weight status does not appear to lower the likeli-
hood of problematic substance use behavior.
Although the current study clearly indicates that
overweight or obese adolescents have a greater
likelihood of being a regular smoker in the absence
of problematic alcohol or marijuana use compared
to non-overweight/obese adolescents, past longi-
tudinal studies have reported mixed findings re-
garding the role of higher weight status on ciga-
rette smoking.35-38 Consideration of alcohol use
and marijuana use alongside cigarette smoking,
and assessment of cigarette smoking behaviors

other than smoking initiation, which was a focus of
previous studies, increases understanding of the
relationship between weight status and cigarette
smoking. One of the main questions arising from
the current findings pertains to why higher weight
status is related specifically to regular cigarette
smoking but not other forms of problematic sub-
stance use. Although there are many hypotheses
linking higher weight status to cigarette smoking,
to date, only a handful of studies present evidence
on the potential pathways by which higher weight
status in adolescence is linked to cigarette smok-
ing. For instance, overweight and obese individu-
als may initiate cigarette smoking because they
perceive it as an effective weight loss strategy.51,52
Also, perception of being overweight or obese and
focus on body size are related to smoking initia-
tion, particularly among adolescent girls.37,53 In
addition to social factors, biological ones also may
inform the relationship between higher weight sta-
tus and regular cigarette smoking. Food and drugs
are known to activate the same neurological path-
ways containing dopaminergic receptors linked
to reward sites in the brain54,55 that may explain
their co-occurrence. For instance, recent studies
examining the pathway from cigarette smoking to
obesity from adolescence to young adulthood sug-
gest that decreased cigarette smoking is associ-
ated with increased weight status.56,57 The negative
relationship between decreased smoking and in-
creased weight status suggest smoking and eating
have similar neurophysiological response systems,
as noted by other studies.58,59 For this reason, bi-
directional relationships between weight status
and cigarette smoking during the transition from

adolescent to young adulthood should be explored
more fully in future studies.
Other findings from the current study revealed
that ethnic and sex differences in substance use
membership were generally in line with previous
findings. Similar to prevalence rates reported in
the National Survey on Drug Use and Health,5 re-
sults showed that African-Americans, Asians, and
Latinos were at lower risk of problematic cigarette,
alcohol, and marijuana use, and males were at
higher risk of belonging to problematic substance
use classes. Future research should seek to as-
sess whether ethnicity and/or sex moderates the
relationship between higher weight status and sub-
stance use, as this is yet unclear. Given that ethnic
and sex disparities exist among substance use, as
well as obesity status,4 these differences may influ-
ence the degree to which risk behaviors are engaged
in by overweight or obese adolescents from specific
ethnic and/or sex groups. For instance, if the objec-
tive to engage in regular cigarette smoking for over-
weight or obese adolescents is to lose weight, this
relationship may be stronger among white females
than other groups, as they are most often subjected
to social stigma and other negative social conse-
quences resulting from higher weight status.60
Of course, several limitations need to be con-
sidered. First, the current study relied on self-
reported height and weight to measure BMI% for
adolescents. Although measured BMI data is fa-
vored over self-report, relatively minor differences
in reliability between self-reported and measured
BMI data have been reported in adolescence40,61
and numerous studies have successfully used

ADD Health self-reported BMI data.62-64 Second, it
is important to note that weight status was only
accounted for at one point in time during adoles-
cence. It is unknown whether adolescents meeting
overweight or obesity status at Wave 1 met over-
weight or obesity status across most or all years of
adolescence. Similarly, we expect that some ado-
lescents meeting overweight or obese status at dif-
ferent points in time during adolescence were not
accounted for in the analyses. BMI data are avail-
able one year after Wave 1 (Wave 2) and 60% of
participants meeting overweight or obesity status
in Wave 1 also met overweight or obesity status in
Wave 2. Third, the study was unable to examine
socio-contextual and neighborhood variables that
may inform how higher weight status contributes
to cigarette smoking risk, such as low peer status
or limited neighborhood resources for maintaining
healthy weight. Fourth, the variation of age in the
sample at each assessment point (eg, 18-26 years
at Wave 3 increases the difficulty in interpreting
findings as a result of developmental milestones,
such as transition to college when high-risk alco-
Lanza et al
hol use increases substantially.
Acknowledging these limitations, the current
study still has important implications for public
health efforts aimed to mitigate health-risk behav-

iors among young adults. Specifically, emphasis
should be placed on decreasing the risk of ciga-
rette smoking in overweight or obese adolescents.
Although more research is needed to identify the
processes by which adolescent weight status influ-
ences cigarette smoking risk, physicians and other
health professionals should address smoking risk
with overweight or obese adolescents and probe for
psychosocial or physiological stressors that may
initiate high-risk smoking activity. Adolescents are
exposed to many psychosocial stressors as they
transition into young adulthood (eg, college, em-
ployment, leaving home). Taking on the responsi-
bilities of their own health behaviors may be too
difficult for some adolescents already negotiating
with significant developmental changes. Over-
weight and obese adolescents may find themselves
dealing with these types of stressors; on top of this
they likely may be experiencing social exclusion
and victimization. The culmination of risks may
contribute to an overweight/obese adolescent’s de-
cision to engage in regular smoking behavior as
a weight management strategy and/or to decrease
anxiety. They may be especially willing to engage
in regular cigarette smoking if they believe smok-
ing has similar effects on their physiological state
as eating does (eg, feeling calm, pleasure). How-
ever, without knowing the health service needs of
overweight and obese adolescents, as well as other
socio-contextual factors that largely influence obe-
sity risk (physical activity resources, access to and
knowledge of healthy nutrition), intervention ef-
forts targeting smoking among overweight or obese
youth will not be as effective. Empirical studies
that can integrate multiple lines of research on
obesity and substance use risk are warranted to

answer questions about preventing and treating
health risks in this unique population.
Human Subjects Statement
The following study has been approved by the
University of California, Los Angeles Institutional
Review Board (#10-001106).
Conflict of Interest Statement
None of the authors have conflict of interests
pertaining to this study.
Acknowledgement
This study is supported by the National Institute
of Drug Abuse (R03DA033497 and T32DA007272).
References
1. Needham BL, Epel ES, Adler NE, Kiefe C. Trajectories of
change in obesity and symptoms of depression: the CAR-
DIA study. Am J Public Health. 2010;100(6):1040-1046.
2. American Diabetes Association. Type 2 diabetes in chil-
dren and adolescents. Pediatrics. 2000;105(3):671-680.
3. Sweeting H, Wright C, Minnis H. Psychosocial correlates
of adolescent obesity, ‘slimming down’ and ‘becoming
obese’. J Adolesc Health. 2005;37(5):409.e9-409.e17.
4. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of
obesity and trends in body mass index among US children
and adolescents, 1999-2010. JAMA. 2012;307(5):483-
490.

5. SAMHSA, Substance Abuse and Mental Health Services
Administration. Results from the 2011 National Survey
on Drug Use and Health: Summary of National Find-
ings. NSDUHSeries H-44, HHS Publication No. (SMA)
12-4713. Rockville, MD: Substance Abuse and Mental
Health Services Administration; 2012.
6. Dick DM, Aliev F, Latendresse SJ, et al. Adolescent alco-
hol use is predicted by childhood temperament factors
before age 5, with mediation through personality and
peers. Alcohol Clin Exp Res. 2013;37(12):2108-2117.
7. Tarter RE, Kirisci L, Mezzich A, et al. Neurobehavioral dis-
inhibition in childhood predicts early age at onset of sub-
stance use disorder. Am J Psychiatry. 2003;160(6):1078-
1085.
8. Glantz MD, Leshner AI. Drug abuse and developmental
psychopathology. Dev. Psychopathol. 2000;12(04):795-
814.
9. Griffin KW, Botvin GJ, Scheier LM, et al. Parenting prac-
tices as predictors of substance use, delinquency, and
aggression among urban minority youth: moderating ef-
fects of family structure and gender. Psychol Addict Be-
hav. 2000;14(2):174.
10. Stice E, Barrera M. A longitudinal examination of the re-
ciprocal relations between perceived parenting and ado-
lescents’ substance use and externalizing behaviors. Dev
Psychol. 1995;31(2):322.
11. Kaplow JB, Curran PJ, Dodge KA. Child, parent, and
peer predictors of early-onset substance use: a mul-
tisite longitudinal study. J Abnorm Child Psychol.

2002;30(3):199-216.
12. Dishion TJ, Owen LD. A longitudinal analysis of friend-
ships and substance use: bidirectional influence from
adolescence to adulthood. Dev Psychol. 2002;38(4):480-
491.
13. Dodge KA, Greenberg MT, Malone PS. Testing an ideal-
ized dynamic cascade model of the development of seri-
ous violence in adolescence. Child Dev. 2008;79(6):1907-
1927.
14. Hawkins JD, Catalano RF, Miller JY. Risk and protective
factors for alcohol and other drug problems in adoles-
cence and early adulthood: implications for substance
abuse prevention. Psychol Bull. 1992;112(1): 64-105.
15. Kilpatrick DG, Acierno R, Saunders B, et al. Risk fac-
tors for adolescent substance abuse and dependence:
data from a national sample. J Consult Clin Psychol.
2000;68(1):19-30.
16. Anzman SL, Birch LL. Low inhibitory control and restric-
tive feeding practices predict weight outcomes. J Pediatr.
2009;155(5):651-656.
17. Cutler DM, Glaeser EL, Shapiro JM. Why have Ameri-
cans become more obese? J Econ Perspect. 2003;17:93-
118.
18. Gottfredson MR, Hirschi T. A general theory of crime.
Stanford, CA: Stanford University Press; 1990.
19. Pratt TC, Cullen FT. The empirical status of Gottfreds-
sons and Hirschi’s General Theory of Crime: a Meta anal-
ysis. Criminology. 2000;38(3):931-964.

20. Steinberg L. A social neuroscience perspective on adoles-
cent risk-taking. Dev Rev. 2008;28(1):78-106.
21. Tajfel H, Turner JC. An integrative theory of intergroup
conflict. In Austin WG Worchel S., (Eds). The Social Psy-
chology of Intergroup Relations. Monterey, CA: Brooks-
Cole; 1979.
22. Tajfel H, Turner JC. The social identity theory of inter-
group behavior. In Worchel S. Austin LS (Eds). Psychol-
Use Patterns?
716
ogy of Intergroup Relations. Chicago: Nelson-Hall; 1986.
23. Sentse M, Scholte R, Salmivalli C, Voeten M. Person-
group dissimilarity in involvement in bullying and its
relation with social status. J Abnorm Child Psychol.
2007;35(6):1009-1019.
24. Van Vilerberghe L, Braet C. Dysfunctional schemas and
psychopathology in referred obese adolescents. Clinical
Psychology and Psychotherapy. 2007;14(5):342-351.
25. Falkner NH, Neumark-Sztainer D, Story M, et al. Social,
educational, and psychological correlates of weight sta-
tus in adolescents. Obes Res, 2001;9(1):32-42.
26. Pearce MJ, Boergers J, Prinstein MJ. Adolescent obesity,
overt and relational peer victimization, and romantic re-

lationships. Obes Res. 2002;10(5):386-393.
27. Strauss RS, Pollack HA. Social marginalization
of overweight children. Arch Pediatr Adolesc Med.
2003;157(8):746-752.
28. Dishion TJ, Medici SN. An ecological analysis of monthly
“bursts” in early adolescent substance use. Appl Dev Sci,
2000;4(2):89-97.
29. Abrantes AM, Brown SA, Tomlinson KL. Psychiatric
comorbidity among inpatient substance abusing ado-
lescents. J Child Adolesc Subst Abuse. 2003;13(2):83-
101.
30. Bizarri JV, Rucci P, Sbrana A, et al. Reasons for sub-
stance use and vulnerability factors in patients with sub-
stance use disorder and anxiety or mood disorder. Addict
Behav. 2006;32(2):384-391.
31. Dhariwal M, Rasmussen M, Holstein BE. Body mass in-
dex and smoking: cross-sectional study of a represen-
tative sample of adolescents in Denmark. Int J Public
Health. 2010;55(4):307-314.
32. Fonseca H, Matos MG, Guerra A, Pedro JG. Are over-
weight and obese adolescents different from their peers?
Pediatr Obes. 2009;4(3):66-174.
33. Liu T, Yen J, Ko C, et al. Associations between substance
use and body mass index: moderating effects of sociode-
mographic characteristics among Taiwanese adoles-
cents. Kaohsiung J Med Sci. 2010;26(6):281-289.
34. Farhat T, Iannotti RJ, Morton-Simons BG. Overweight,
obesity, youth, and health-risk behaviors. Am J Prev

Med. 2010;38(3):258-267.
35. Austin SB, Gortmaker SL. Dieting and smoking initiation
in early adolescent girls and boys: a prospective study.
Am J Public Health. 2001;91(3):446-450.
36. Caria MP, Bellocco R, Zambon A, et al. Overweight and
perception of overweight as predictors of smokeless to-
bacco use and of cigarette smoking in a cohort of Swed-
ish adolescents. Addiction. 2009;104(4):661-668.
37. Cawley J, Markowitz S, Tauras J. Lighting up and slim-
ming down: the effects of body weight and cigarette
prices on adolescent smoking initiation. J Health Econ.
2004;23(2):293-311.
38. Pasch KE, Velazquez CE, Cance JD, et al. Youth sub-
stance use and body composition: does risk in one area
predict risk in the other? J Youth Adolesc. 2012;41(1):14-
26.
39. Mustillo S, Worthman C, Erkanli A, et al. Obesity and
psychiatric disorder: developmental trajectories. Pediat-
rics. 2003;111(4):851-859.
40. Goodwin E, Hinden BR, Khandewal S. Accuracy of teen
and parental reports of obesity and body mass index.
Pediatrics. 2000;106(1):52-58.
41. Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, et al.
CDC Growth Charts: United States Advance Data from Vi-
tal and Health Statistics. No. 314. Atlanta, GA: National
Center for Health Statistics; 2000.
42. Hagenaars JA, McCutcheon AL. Applied Latent Class
Analysis. Cambridge: Cambridge University Press; 2002.

43. Lanza ST, Collins LM, Lemmon D, Schafer JL. PROC
LCA: a SAS procedure for latent class analysis. Struct
Equ Modeling. 2007;14(4):671-694.
44. Nylund K, Bellmore A, Nishina A, Graham S. Sub-
types, severity, and structural stability of peer victim-
ization: what does latent class analysis say? Child Dev.
2007;78(6):1706-1722.
45. Muthén LK, Muthén BO. Mplus User’s Guide (7th ed.). Los
Angeles, CA: Muthén & Muthén; 1998-2012.
46. Collins LM, Lanza ST. Latent Class and Latent Transition
Analysis: For Applications in the Social, Behavioral, and
Health Sciences. Hoboken, NJ: Wiley; 2009.
47. Schwartz G. Estimating the dimension of the model. Ann
Stat. 1978;6(2):461-464.
48. Lo Y, Mendell NR, Rubin DB. Testing the num-
ber of components in a normal mixture. Biometrika.
2001;88(3):767-778.
49. Wang Y. Cross-national comparison of childhood obesity:
the epidemic and the relationship between obesity and
socioeconomic status. Int J Epidemiol. 2001;30(5):1129-
1136.
50. Giang MT, Graham S. Using latent class analysis to iden-
tify aggressors and victims of peer harassment. Aggress
Behav. 2008;34(2):203-213.
51. Lowry R, Galuska DA, Fulton JE, et al. Weight man-
agement goals and practices among U.S. high school

students: associations with physical activity, diet, and
smoking. J Adolesc Health. 2002;31(2):133-144.
52. Plotnikoff RC, Bercovitz K, Rhodes RE, et al. Testing a
conceptual model related to weight perceptions, physical
activity and smoking in adolescents. Health Educ Res.
2007;22(2):192-202.
53. French SA, Perry CL, Leon GR, Fulkerson JA. Weight
concerns, dieting behavior, and smoking initiation
among adolescents: a prospective study. Am J Public
Health. 1994;84(11):1818-1820.
54. Epstein LH, Jaroni JL, Paluch RA, et al. Dopamine trans-
porter genotype as a risk factor for obesity in African-
American smokers. Obes Res. 2002;10(12):1232-1240.
55. Volkow ND, Wise RA. How can drug addiction help us
understand obesity? Nat Neurosci. 2005;8(5):555-560.
56. Huang DY, Lanza HI, Anglin MD. Association between
adolescent substance use and obesity in young adult-
hood: a group-based dual trajectory analysis. Addict Be-
hav. 2013;38(11):2653-2660.
57. Kvaavik E, Tell GS, Klepp KI. Predictors and tracking of
body mass index from adolescence into adulthood: fol-
low-up of 18 to 20 years in the Oslo Youth Study. Arch
Pediatr Adolesc Med. 2013;157(12):1212-1218.
58. Mineur YS, Abizaid A, Rao Y, et al. Nicotine decreases
food intake through activation of POMC neurons. Sci-
ence. 2011;332(6035):1330-1332.
59. Seeley RJ, Sandoval DA. Neuroscience: weight loss
through smoking. Nature. 2011;475(7355):176-177.

60. Strauss RS, Pollack HA. Social marginalization
of overweight children. Arch Pediatr Adolesc Med.
2003;157(8):746-752.
61. Field AE, Aneja P, Rosner B. The validity of self-reported
weight change among adolescents and young adults.
Obesity. 2007;15(9):2357-2364.
62. Harris KM, Perreira K M, Lee D. Obesity in the transi-
tion to adulthood: predictions across race/ethnicity, im-
migrant generation, and sex. Arch Pediatr Adolesc Med.
2009;163(11):1022-1028.
63. Merten MJ. Weight status continuity and change from
adolescence to young adulthood: examining disease and
health risk conditions. Obesity. 2010;18(7):1423-1428.
64. Tanner-Smith EE. Negotiating the early developing body:
pubertal timing, body weight, and adolescent girls’ sub-
stance use. J Youth Adolesc. 2010;39(12):1402-1416.
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Lease Versus Purchase Paper Grading Guide

FIN/370 Version 8
1
Learning Team: Lease Versus Purchase Paper
Purpose of Assignment
Students compare the factors involved in leasing versus
purchasing capital assets.
Resources Required
Ch. 27 of Basic Finance
Grading Guide
Content
70 Percent
Met
Partially Met
Not Met
Comments:
Discuss the factors involved in making a lease versus purchase
decision.
Discuss the application of time value of money concepts used in
evaluating lease versus purchase decisions.
The paper is no more than 350 words in length.

Total Available
Total Earned
X
#/X
Writing Guidelines
30 Percent
Met
Partially Met
Not Met
Comments:
The paper—including tables and graphs, headings, title page,
and reference page—is consistent with APA formatting
guidelines and meets course-level requirements.
Intellectual property is recognized with in-text citations and a
reference page.
Paragraph and sentence transitions are present, logical, and
maintain the flow throughout the paper.

Sentences are complete, clear, and concise.
Rules of grammar and usage are followed including spelling and
punctuation.
Total Available
Total Earned
X
#/X
Assignment Total
#
X
#/5
Additional comments:
Copyright © 2014 by University of Phoenix. All rights reserved.
Intermediate-Term Debt and Leasing
The previous chapter covered various short-term sources of

finance, commercial bank loans, trade credit, commercial paper,
secured loans, and factoring. Chapters 12 through 15 covered a
variety of long-term, fixed-income securities ranging from
straight bonds to convertible bonds and preferred stock.
Between these extremes are intermediate-term bonds and
leasing. Intermediate-term debt ranges from 5 to 10 years and is
obtained through commercial banks and insurance companies.
Term notes may also be sold to the general public.
Some firms own properties in order to lease the properties to
others. Washington Real Estate Trust owns office buildings,
industrial centers, apartments, and shopping centers in the
Washington, D.C., area and rents the space to stores such as
Williams-Sonoma, Laura Ashley, and Giant Food. These retail
establishments want the use of the asset but not ownership.
They prefer to lease the space instead of buying and operating
the buildings.
The term of a lease may range from a short period such as a
year or two to many years. Since leases create legal obligations
(the lease payments), they are an alternative to short-,
intermediate-, and long-term debt as a source of funds. This
chapter describes the terms of a lease and illustrates the basic
analysis of leasing versus borrowing and buying the asset. Since
leases are an alternative to debt financing, this section also
covers when lease obligations must be capitalized and placed on
the firm’s balance sheet as a debt obligation.
Intermediate-Term Debt
While accountants classify all liabilities as either short-term
(due in less than one year) or long-term (due in more than one
year), debt can also be classified as short-term, intermediate-
term, or long-term. Intermediate-term debt is outstanding for
more than a year (and hence appears as long-term debt on the
firm’s balance sheet), but it matures quicker than long-term
debt. While long-term bonds may mature 20, 25, or 30 years
after being issued, most intermediate-term debt will mature in 5
to 10 years.
Intermediate-term debt issued by corporations and sold to the

general public may be referred to as “notes” to differentiate it
from the bonds of the corporation, which are long-term debt.
For example, in July 2008, H. J. Heinz issued $500 million of
5.35 percent notes due in 2013 (5 years to maturity). Different
terminology may be used when intermediate-term debt is
obtained from a commercial bank or an insurance company.
Such debt is often referred to as a term loan.
Term loans are usually secured by equipment or real estate.
Commercial banks, which make term loans of 1 to 5 years’
duration, generally require that the loan be secured by
equipment. Insurance companies, which tend to make term loans
of from 5 to 15 years, generally use real estate as collateral for
the loan.
In addition to the collateral, term loans have restrictive
covenants that are negotiated between the debtor and the
creditor. Common restrictions include a minimum current ratio
such as 2.0:1, or a minimum amount of net working capital (that
is, the difference between current assets and current liabilities
must exceed some specified dollar amount). The creditors also
require periodic financial statements from the borrower and may
require prior approval before the debtor can issue additional
debt. While these restrictive covenants are common in term loan
agreements, they do not exhaust all the possibilities, as each
loan is individually negotiated. Conditions in the credit markets
and the relative strengths of the parties also affect the terms.
Term loans are generally retired in periodic payments and hence
are like mortgage loans. The repayment schedules call for the
payment of interest and the retirement of the principal. For
example, a firm buys equipment that costs $12,000 and has an
expected life of 5 years. The firm arranges a term loan with a
commercial bank. The following conditions apply:
1. a down payment of 20 percent of the cost of the equipment
2. five equal annual payments to pay the interest and retire the
loan
3. a 9 percent interest rate on the declining balance
4. the loan to be secured by the equipment

The first condition establishes the amount that the bank is
willing to loan. Notice that the bank does not lend the entire
amount; the borrower must put up $2,400 (0.20 × $12,000) and
the bank finances the balance, $9,600. Terms 2 and 3 establish
the rate of interest and the payment schedule. The fourth term
designates the equipment as collateral against the loan and gives
the bank the right to take the equipment and sell it should the
debtor default.
The repayment schedule is determined as follows. The borrower
must make equal payments so that the bank earns 9 percent
annually and the loan is retired in 5 years. This is another
illustration of the time value of money. The equation necessary
to solve this problem (that is, to determine the annual payments)
is
Calculator
Solution
Function Key
Data Input
PV =
–9,600
FV =
0
I =
9
N =

5
PMT =
?
Function Key
Answer
PMT =
2468.09
This is an example of an annuity, so the problem collapses to:
PMT(PVAIF 9I, 5N) =
$9,600
PMT(3.890) =
$9,600
PMT =
$9,600/3.890 = $2,467.87.
Thus, $2,467.87 is the annual payment that retires this loan and
pays 9 percent of the declining balance.
The actual payment schedule and the division of the payment
into interest payment and principal reduction are given
in Exhibit 27.1. This table is essentially the same as the
mortgage loan amortization schedule illustrated in Exhibit
7.2 (page 124) in Chapter 7. In both examples, the amount of
the interest declines with each payment as the outstanding
balance on the loan is reduced. Conversely, the amount of the
principal repayment rises with each payment as the interest
payment is reduced.

Generally, the depreciation of the equipment and the resulting
cash flow cover the required loan payments. In this case, the
annual straight-line depreciation expense would be $2,400
($12,000/5). The cash flow generated by this $2,400 noncash
depreciation expense is approximately equal to the $2,467.87
payment required by the loan. (In many cases accelerated
depreciation is used so that the initial depreciation expense is
increased.) By matching the repayment schedule with the cash
flow, the firm enhances its capacity to service the debt.
Exhibit 27.1 Repayment Schedule for a $9,600 Term Loan at 9
Percent for Five Years
Year
Payment
Interest
Principal Retirement
Balance Owed on Loan
1
$2,467.87
$864.00
$1,603.87
$7,996.13
2
2,467.87
719.65
1,748.22

6,247.91
3
2,467.87
562.31
1,905.56
4,342.35
4
2,467.87
390.81
2,077.06
2,265.29
5
2,467.87
203.87
2,264.00
1.29*
*The $1.29 results from rounding off in using the interest
tables. The $2,468.09 payment determined by the financial
calculator avoids this error.
Since each loan is individually negotiated between the borrower
and the lender, a variety of possible terms exist. One possibility
is for the lender to require equal principal repayments with
interest being computed on the remaining balance for each
period. The repayment schedule under these terms for the
$9,600 term loan is presented in Exhibit 27.2. In this case, the

principal is retired in five equal installments of $1,920
($9,600/5 = $1,920 in the second column). The amount of
interest (column 3) depends on the balance owed (column 4).
Thus, the payment in the second year is the sum of the principal
repayment ($1,920) plus the interest on the balance owed at the
end of the first year ($691.20), for a total payment of $2,611.20
(column 5).
Exhibit 27.2 Repayment Schedule for a $9,600 Term Loan at 9
Percent with Equal Principal Repayments
Year
Principal Repayment
Interest
Balance of Loan
Total Payment
1
$1,920
$864.00
$7,680
$2,784.00
2
1,920
691.20
5,760
2,611.20
3

1,920
518.40
3,840
2,438.40
4
1,920
345.60
1,920
2,265.60
5
1,920
172.80
0
2,092.80
Other possible terms include no principal repayment until the
loan is due at the end of the fifth year. In this case, the firm
would annually remit the $864 interest payment, and at the end
of the fifth year make the last interest payment plus the
principal repayment ($864 + $9,600 = $10,464). The lender
could combine the two previous illustrations and annually
require a partial principal repayment (for example, $1,000
annually) with the balance of $4,600 ($9,600 – $5,000) paid at
the end of the term of the loan. Such a lump repayment at the
end of a loan is referred to as a balloon payment.
Although firms obtain intermediate-term credit from banks and

insurance companies, intermediate-term securities may be sold
to the general public. Notes sold to the general public are not
collateralized, while term loans usually are, and generally the
notes do not have a compulsory repayment schedule. Such notes
are really more similar to long-term bonds than to term loans.
However, these notes may have specific features that make them
attractive to investors. The intermediate term (for example, 7
years) may make these notes attractive to investors who do not
want to make investments for a longer term (such as 20 years).
In addition, intermediate-term notes frequently cannot be called
and refunded before maturity. Since the notes lack a call
feature, the investor knows that the firm cannot force the buyer
to give up the security should interest rates fall. Many long-
term bonds are called and refunded when long-term interest
rates fall, so this noncallability of intermediate-term notes
assures investors of their interest income (if no default) for the
term of the notes.
Leasing
Leasing is essentially renting, and the two terms are often
interchanged. Since lease contracts may cover any time period,
lease financing may be an alternative to short- or long-term
debt.
A lease contract is for the use of an asset such as plant or
equipment. Firms want the use of the asset. They use the capital
budgeting techniques (net present value and internal rate of

return) to determine which investments are profitable. After
deciding which investments to make, they must decide how to
finance the asset. Notice that it is the use of the asset that the
firm desires and not necessarily title to the asset. Leasing
permits the firm (lessee) to use the asset without acquiring title,
which is retained by the owner (the lessor). In return, the lessee
enters into a contract (thelease) to make specified payments for
the use of the asset.
Leases take one of two forms. An operating lease provides the
lessee with the use of the asset and may include a maintenance
contract. The cost of servicing the equipment is built into the
lease. The contract may be canceled after proper notice if the
lessee wants to change equipment. This type of lease is
primarily used for renting equipment, cars, and trucks. The
length of the lease is less than the expected life of the asset but
the lease may be renewed. Since the lease is not for the life of
the asset, the lessor anticipates either having the lease renewed
or selling the asset at the lease’s expiration.
A financial lease, which may also be referred to as
a capital lease, differs from an operating lease in several
significant ways. These contracts are not cancelable and do not
include a service clause. The duration of a financial lease is the
expected life of the asset. The lease payments cover the cost of
the asset and earn a set return for the lessor. Thus, a financial
lease is similar to debt financing. If the firm had issued bonds

to obtain the funds to acquire the asset, the payments to the
bondholders would cover the cost of the equipment plus their
return (the rate of interest). Of course, if debt had been used,
the firm would own the asset, while with leasing it does not
acquire title. This difference is important if at the end of the
asset’s life there is residual value that accrues to the asset’s
owner.
While there are two classes of leases, there are three types of
lease agreements. From the viewpoint of the lessee, the type of
lease agreement is immaterial; the firm still acquires the use of
the asset. The type of lease has an impact only on the lessor.
The first type of lease agreement is thedirect lease. The lessor
owns the asset and directly leases it to the lessee. Direct leases
are offered by manufacturers who build the asset, such as IBM,
as well as by finance companies and leasing companies that
acquire assets with the intent to lease them to prospective users.
The second type of lease is a sale and leaseback. Under this
type of agreement, the firm that owns the asset sells it to the
lessor and then leases it back. The selling firm receives cash
from the sale to the lessor that can be put to other uses but still
retains the use of the asset. The lessee, however, relinquishes
title to the asset and thus loses any residual value that the asset
might have. And, of course, the firm must now make the lease
payments.
The third type of lease is a leveraged lease. Since the lessor

owns the asset, that firm must have the funds to acquire it. In a
leveraged lease the lessor borrows part of the funds necessary to
acquire the asset. For example, a finance company may borrow
from a commercial bank so that it may acquire an asset that it in
turn leases to the ultimate users. If financial leverage is
favorable, the lessor will increase the return on its funds
invested in the asset.
Lease or Purchase
The question of whether it is better to buy or to lease depends
on several crucial variables. These include the firm’s tax
bracket, the terms of the lease, the asset’s anticipated residual
value, and the cost of obtaining funds to buy the asset. While
this introductory text cannot develop this topic, the following
example will provide some of the essential information
necessary to make the choice.
A firm decides to acquire equipment that costs $5,000. The
equipment has an expected life of five years, after which the
equipment will be sold for an expected salvage value of $500.
Depreciation will be straight-line. (The firm would use
accelerated depreciation if possible, and the depreciation
expense would start after six months have elapsed. These
normal conditions are ignored to simplify the illustration.)
Maintenance is expected to be $200 annually, and the firm’s tax
rate is 40 percent. The purchase is financed entirely with a
$5,000 loan that is retired through an annual $1,285 payment

covering interest and principal (that is, the payments are a
mortgage schedule). The annual cash outflows and inflows are
shown in Exhibit 27.3.
Initially, there is an immediate cash $5,000 outflow to pay for
the equipment but that is covered by the loan, so there is no
immediate cash outflow. At the end of the first year, there is a
$200 cash outflow for maintenance, $450 for interest, and $835
for principal repayment, for a total of $1,485. Notice that the
$1,000 depreciation is not a cash outflow because it is a
noncash expense.
Exhibit 27.3 Determination of Cash Outflows from Owning
The outflows are partially offset by the tax savings that result
from tax-deductible expenses. These expenses are the $200
maintenance, the $450 interest, and the $1,000 depreciation.
Notice the principal repayment is not tax deductible. The sum of
the tax deductible expense is $1,650, and since the tax rate is 40
percent, these expenses reduce income taxes by $660 so the net
cash outflow is $825 ($1,485 – 660). Notice that the net cash
outflow grows each year because the principal repayment
increases and it is not a tax-deductible expense.
At the end of the fifth year, the equipment is sold for $500. The
sale is a cash inflow, but if an asset is sold for more than its
book value, the cash inflow is reduced by the taxes generated by
the sale. (If the asset were sold for less than its book value, the

sale would reduce taxes.) In this illustration, the asset is
completely depreciated, so its book value is $0. All of the sale
is taxable income, so the firm nets only $300 after paying taxes
of $200 on the $500 sale.
Alternatively, the firm could lease the equipment from a lessor
who wants a 10 percent return. To determine the annual lease
payments, the lessor answers the following question: How much
must I charge each year so that my $5,000 invested in the
equipment yields 10 percent? (There is no reason to assume the
interest rate lessee pays to borrow is the same rate the lessor
wants to earn on the lease.) That is,
The interest factor is 3.791, so the equation becomes
PMT(3.791) =
$1,319
PMT = $5,000/3.791 =
$1,319.
For the lessor to earn 10 percent, the annual lease payment
should be $1,319.
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